Human posture estimation based on image data from a captured video sequence has been an active area of research in recent years. This is because being able to determine human behavior based on videos through computer analysis would make behavior analysis, which is performed in various fields, possible without requiring human effort. Examples of behavior analysis include abnormal behavior detection on the streets, purchasing behavior analysis in stores, factory streamlining support, and form coaching in sports.
In this respect, PL 1, for example, discloses a technique for estimating the posture state of a person based on image data captured with a monocular camera. In the technique disclosed in PL 1 (hereinafter referred to as “related art technique”), part candidates are first extracted based on elliptical shapes or parallel lines contained in the captured image. Next, the related art technique uses a likelihood function, which is statistically derived from a plurality of sample images, to compute part likelihoods and part relation likelihoods. The related art technique then computes the optimal combination of part candidates based on these likelihoods. The use of the above-mentioned related art technique enables identification as to which part is located at which region, as well as estimation of the posture state of a human regardless of location or orientation.